Enhancing {ggplot2} plots with statistical analysis 📊📣
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Updated
Apr 14, 2025 - R
Enhancing {ggplot2} plots with statistical analysis 📊📣
A Julia implementation of the Targeted Minimum Loss-based Estimation
📦 Simultaneous Confidence Bands Based on the Efficient Influence Function and Multiplier Bootstrap 📚 🔮 📡
Hands-on Instructions on Different courses
This repo is for copula based analysis on bivariate as well as multivariate data sets in ecology and related fields. For details and citation we refer to this publication: Ghosh et al., Advances in Ecological Research, vol 62,pp 409, 2020
Hypothesis Testing CrossFit Game 2015
Reproducible materials for Nonparametric Statistical Inference via Metric Distribution Function in Metric Spaces (JASA, 2023+)
A Simple and Flexible Test of Sample Exchangeability with Applications to Statistical Genomics
Median Test in R Software
The Expectation Maximization (EM) algorithm is used to reduce Poisson noise in CT images. The repository provides derivations and evaluations with the Cramer-Rao Lower Bound (CRLB).
This project offers an R code implementation for estimating the density function using the kernel method. It explores different values of the parameters h and n to perform these estimations.
MSc Thesis: Non-Parametric Estimation of Optimal Individualized Treatment Rules for Survival Data
Correlated Zero Inflated Negative Binomial Process
Non-parametric estimation of density using kernels and histograms with Python and R.
Program Evaluation with Non-Parametric Statistics and Hypothesis Testing
🛠「NopaPy」是一个开源易用的非参数统计Python库。
Hypothesis Testing with Men's and Women's Soccer Matches
All my R code used for statistical analyses - Hypothesis Testing, t-tests, etc.
Accessible implementation of statistical learning algorithms, non-parametric and high-dimensional methods.
The shared area under probability distribution curves test is a non-parametric alternative to the analysis of variance. It makes no assumptions about the data and it is not based on ranks, so information about absolute distances between data points is not lost.
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